Image classification is a crucial task in modern weed management and crop
intervention technologies. However, the limited size, diversity, and balance of
existing weed datasets hinder the development of deep learning models for
generalizable weed identification. In addition, the expensive labelling
requirements of mainstream fully-supervised weed classifiers make them cost-
and time-prohibitive to deploy widely, for new weed species, and in
site-specific weed management. This paper proposes a novel method for Weed
Contrastive Learning through visual Representations (WeedCLR), that uses
class-optimized loss with Von Neumann Entropy of deep representation for weed
classification in long-tailed datasets. WeedCLR leverages self-supervised
learning to learn rich and robust visual features without any labels and
applies a class-optimized loss function to address the class imbalance problem
in long-tailed datasets. WeedCLR is evaluated on two public weed datasets:
CottonWeedID15, containing 15 weed species, and DeepWeeds, containing 8 weed
species. WeedCLR achieves an average accuracy improvement of 4.3\% on
CottonWeedID15 and 5.6\% on DeepWeeds over previous methods. It also
demonstrates better generalization ability and robustness to different
environmental conditions than existing methods without the need for expensive
and time-consuming human annotations. These significant improvements make
WeedCLR an effective tool for weed classification in long-tailed datasets and
allows for more rapid and widespread deployment of site-specific weed
management and crop intervention technologies.

使用深度学习模型对长尾数据集中的杂草进行分类，提出了一种基于类优化损失和深层表示的 WeedCLR 方法，在无标签数据上使用自监督学习来学习视觉特征，并通过类优化损失函数解决长尾数据集中的类别不平衡问题，相较于先前的方法在不同环境条件下展现出更好的泛化能力和稳健性，无需昂贵且耗时的人工标注，可广泛应用于场地特定的杂草管理和作物干预技术。